Overview

Dataset statistics

Number of variables24
Number of observations2059
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory386.2 KiB
Average record size in memory192.1 B

Variable types

Categorical7
Text2
Numeric15

Alerts

Age of Car is highly overall correlated with Kilometer and 2 other fieldsHigh correlation
Drivetrain is highly overall correlated with Engine and 6 other fieldsHigh correlation
Engine is highly overall correlated with Drivetrain and 11 other fieldsHigh correlation
Fuel Tank Capacity is highly overall correlated with Drivetrain and 10 other fieldsHigh correlation
Height is highly overall correlated with Make and 1 other fieldsHigh correlation
Kilometer is highly overall correlated with Age of Car and 1 other fieldsHigh correlation
Length is highly overall correlated with Engine and 7 other fieldsHigh correlation
Make is highly overall correlated with Drivetrain and 6 other fieldsHigh correlation
Max Power bhp is highly overall correlated with Drivetrain and 10 other fieldsHigh correlation
Max Power rpm is highly overall correlated with Engine and 5 other fieldsHigh correlation
Max Torque Nm is highly overall correlated with Drivetrain and 11 other fieldsHigh correlation
Max Torque rpm is highly overall correlated with Engine and 8 other fieldsHigh correlation
Price is highly overall correlated with Age of Car and 10 other fieldsHigh correlation
Seating Capacity is highly overall correlated with HeightHigh correlation
Transmission is highly overall correlated with Engine and 7 other fieldsHigh correlation
Volume is highly overall correlated with Drivetrain and 9 other fieldsHigh correlation
Width is highly overall correlated with Drivetrain and 10 other fieldsHigh correlation
Year is highly overall correlated with Age of Car and 2 other fieldsHigh correlation
Fuel Type is highly imbalanced (61.5%)Imbalance
Owner is highly imbalanced (64.4%)Imbalance
Seller Type is highly imbalanced (86.9%)Imbalance

Reproduction

Analysis started2025-12-07 05:46:28.160303
Analysis finished2025-12-07 05:49:23.806793
Duration2 minutes and 55.65 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

Make
Categorical

High correlation 

Distinct33
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size16.2 KiB
Maruti Suzuki
440 
Hyundai
349 
Mercedes-Benz
171 
Honda
158 
Toyota
132 
Other values (28)
809 

Length

Max length13
Median length10
Mean length7.9791161
Min length2

Characters and Unicode

Total characters16429
Distinct characters44
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.1%

Sample

1st rowHonda
2nd rowMaruti Suzuki
3rd rowHyundai
4th rowToyota
5th rowToyota

Common Values

ValueCountFrequency (%)
Maruti Suzuki440
21.4%
Hyundai349
16.9%
Mercedes-Benz171
 
8.3%
Honda158
 
7.7%
Toyota132
 
6.4%
Audi127
 
6.2%
BMW121
 
5.9%
Mahindra119
 
5.8%
Tata57
 
2.8%
Volkswagen50
 
2.4%
Other values (23)335
16.3%

Length

2025-12-07T11:19:24.347523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
maruti440
17.4%
suzuki440
17.4%
hyundai349
13.8%
mercedes-benz171
 
6.8%
honda158
 
6.2%
toyota132
 
5.2%
audi127
 
5.0%
bmw121
 
4.8%
mahindra119
 
4.7%
tata57
 
2.3%
Other values (25)418
16.5%

Most occurring characters

ValueCountFrequency (%)
u1878
 
11.4%
a1687
 
10.3%
i1532
 
9.3%
d1045
 
6.4%
n954
 
5.8%
e886
 
5.4%
M884
 
5.4%
r855
 
5.2%
t694
 
4.2%
o657
 
4.0%
Other values (34)5357
32.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)16429
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
u1878
 
11.4%
a1687
 
10.3%
i1532
 
9.3%
d1045
 
6.4%
n954
 
5.8%
e886
 
5.4%
M884
 
5.4%
r855
 
5.2%
t694
 
4.2%
o657
 
4.0%
Other values (34)5357
32.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)16429
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
u1878
 
11.4%
a1687
 
10.3%
i1532
 
9.3%
d1045
 
6.4%
n954
 
5.8%
e886
 
5.4%
M884
 
5.4%
r855
 
5.2%
t694
 
4.2%
o657
 
4.0%
Other values (34)5357
32.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)16429
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
u1878
 
11.4%
a1687
 
10.3%
i1532
 
9.3%
d1045
 
6.4%
n954
 
5.8%
e886
 
5.4%
M884
 
5.4%
r855
 
5.2%
t694
 
4.2%
o657
 
4.0%
Other values (34)5357
32.6%

Model
Text

Distinct1050
Distinct (%)51.0%
Missing0
Missing (%)0.0%
Memory size16.2 KiB
2025-12-07T11:19:27.252712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length50
Median length38
Mean length21.155415
Min length4

Characters and Unicode

Total characters43559
Distinct characters72
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique613 ?
Unique (%)29.8%

Sample

1st rowAmaze 1.2 VX i-VTEC
2nd rowSwift DZire VDI
3rd rowi10 Magna 1.2 Kappa2
4th rowGlanza G
5th rowInnova 2.4 VX 7 STR [2016-2020]
ValueCountFrequency (%)
at247
 
2.9%
1.2176
 
2.1%
plus170
 
2.0%
petrol134
 
1.6%
sx133
 
1.6%
vxi131
 
1.5%
tdi123
 
1.4%
1.6120
 
1.4%
diesel115
 
1.4%
o107
 
1.3%
Other values (650)7038
82.9%
2025-12-07T11:19:31.098275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6439
 
14.8%
i2039
 
4.7%
e1885
 
4.3%
01806
 
4.1%
21765
 
4.1%
r1651
 
3.8%
a1546
 
3.5%
o1442
 
3.3%
11369
 
3.1%
t1347
 
3.1%
Other values (62)22270
51.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)43559
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6439
 
14.8%
i2039
 
4.7%
e1885
 
4.3%
01806
 
4.1%
21765
 
4.1%
r1651
 
3.8%
a1546
 
3.5%
o1442
 
3.3%
11369
 
3.1%
t1347
 
3.1%
Other values (62)22270
51.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)43559
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6439
 
14.8%
i2039
 
4.7%
e1885
 
4.3%
01806
 
4.1%
21765
 
4.1%
r1651
 
3.8%
a1546
 
3.5%
o1442
 
3.3%
11369
 
3.1%
t1347
 
3.1%
Other values (62)22270
51.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)43559
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6439
 
14.8%
i2039
 
4.7%
e1885
 
4.3%
01806
 
4.1%
21765
 
4.1%
r1651
 
3.8%
a1546
 
3.5%
o1442
 
3.3%
11369
 
3.1%
t1347
 
3.1%
Other values (62)22270
51.1%

Price
Real number (ℝ)

High correlation 

Distinct567
Distinct (%)27.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1596608
Minimum195000
Maximum8084000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.2 KiB
2025-12-07T11:19:31.629637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum195000
5-th percentile250000
Q1484999
median825000
Q31925000
95-th percentile5900000
Maximum8084000
Range7889000
Interquartile range (IQR)1440001

Descriptive statistics

Standard deviation1778805.3
Coefficient of variation (CV)1.1141153
Kurtosis3.7045726
Mean1596608
Median Absolute Deviation (MAD)465000
Skewness2.0228591
Sum3.2874159 × 109
Variance3.1641484 × 1012
MonotonicityNot monotonic
2025-12-07T11:19:32.114641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19500045
 
2.2%
808400042
 
2.0%
42500026
 
1.3%
62500024
 
1.2%
65000022
 
1.1%
37500020
 
1.0%
45000020
 
1.0%
67500019
 
0.9%
55000019
 
0.9%
25000019
 
0.9%
Other values (557)1803
87.6%
ValueCountFrequency (%)
19500045
2.2%
1980001
 
< 0.1%
1990001
 
< 0.1%
2000004
 
0.2%
2049991
 
< 0.1%
2100004
 
0.2%
2150001
 
< 0.1%
2180001
 
< 0.1%
2200004
 
0.2%
2250007
 
0.3%
ValueCountFrequency (%)
808400042
2.0%
80000001
 
< 0.1%
79989991
 
< 0.1%
79900001
 
< 0.1%
79000001
 
< 0.1%
77800001
 
< 0.1%
77000001
 
< 0.1%
75000003
 
0.1%
74750002
 
0.1%
73900001
 
< 0.1%

Year
Real number (ℝ)

High correlation 

Distinct22
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2016.4254
Minimum1988
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.2 KiB
2025-12-07T11:19:32.794482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1988
5-th percentile2011
Q12014
median2017
Q32019
95-th percentile2021
Maximum2022
Range34
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.3635636
Coefficient of variation (CV)0.0016680823
Kurtosis2.9266435
Mean2016.4254
Median Absolute Deviation (MAD)2
Skewness-0.84068453
Sum4151820
Variance11.31356
MonotonicityNot monotonic
2025-12-07T11:19:33.522086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
2018268
13.0%
2017262
12.7%
2019218
10.6%
2014192
9.3%
2016187
9.1%
2015178
8.6%
2021156
7.6%
2020132
6.4%
2013128
6.2%
201292
 
4.5%
Other values (12)246
11.9%
ValueCountFrequency (%)
19881
 
< 0.1%
19961
 
< 0.1%
20001
 
< 0.1%
20021
 
< 0.1%
20041
 
< 0.1%
20062
 
0.1%
20076
 
0.3%
200813
 
0.6%
200933
1.6%
201027
1.3%
ValueCountFrequency (%)
202281
 
3.9%
2021156
7.6%
2020132
6.4%
2019218
10.6%
2018268
13.0%
2017262
12.7%
2016187
9.1%
2015178
8.6%
2014192
9.3%
2013128
6.2%

Kilometer
Real number (ℝ)

High correlation 

Distinct779
Distinct (%)37.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52247.446
Minimum4416
Maximum130000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.2 KiB
2025-12-07T11:19:34.319508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4416
5-th percentile8000
Q129000
median50000
Q372000
95-th percentile107991.1
Maximum130000
Range125584
Interquartile range (IQR)43000

Descriptive statistics

Standard deviation29767.999
Coefficient of variation (CV)0.56975033
Kurtosis-0.21143892
Mean52247.446
Median Absolute Deviation (MAD)22000
Skewness0.47520279
Sum1.0757749 × 108
Variance8.8613379 × 108
MonotonicityNot monotonic
2025-12-07T11:19:35.185506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13000043
 
2.1%
441642
 
2.0%
6500033
 
1.6%
7200032
 
1.6%
5000029
 
1.4%
7500029
 
1.4%
4500029
 
1.4%
4200028
 
1.4%
7000026
 
1.3%
5500026
 
1.3%
Other values (769)1742
84.6%
ValueCountFrequency (%)
441642
2.0%
45002
 
0.1%
46002
 
0.1%
50006
 
0.3%
55002
 
0.1%
56001
 
< 0.1%
57501
 
< 0.1%
57901
 
< 0.1%
58001
 
< 0.1%
59231
 
< 0.1%
ValueCountFrequency (%)
13000043
2.1%
1280001
 
< 0.1%
1270001
 
< 0.1%
1265351
 
< 0.1%
1260001
 
< 0.1%
1253721
 
< 0.1%
1250005
 
0.2%
1240001
 
< 0.1%
1230001
 
< 0.1%
1220002
 
0.1%

Fuel Type
Categorical

Imbalance 

Distinct9
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size16.2 KiB
Diesel
1049 
Petrol
942 
CNG
 
50
Electric
 
7
LPG
 
5
Other values (4)
 
6

Length

Max length12
Median length6
Mean length5.9339485
Min length3

Characters and Unicode

Total characters12218
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.1%

Sample

1st rowPetrol
2nd rowDiesel
3rd rowPetrol
4th rowPetrol
5th rowDiesel

Common Values

ValueCountFrequency (%)
Diesel1049
50.9%
Petrol942
45.8%
CNG50
 
2.4%
Electric7
 
0.3%
LPG5
 
0.2%
Hybrid3
 
0.1%
CNG + CNG1
 
< 0.1%
Petrol + CNG1
 
< 0.1%
Petrol + LPG1
 
< 0.1%

Length

2025-12-07T11:19:36.002987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-07T11:19:36.603506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
diesel1049
50.8%
petrol944
45.7%
cng53
 
2.6%
electric7
 
0.3%
lpg6
 
0.3%
hybrid3
 
0.1%
3
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e3049
25.0%
l2000
16.4%
i1059
 
8.7%
D1049
 
8.6%
s1049
 
8.6%
r954
 
7.8%
t951
 
7.8%
P950
 
7.8%
o944
 
7.7%
G59
 
0.5%
Other values (11)154
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)12218
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e3049
25.0%
l2000
16.4%
i1059
 
8.7%
D1049
 
8.6%
s1049
 
8.6%
r954
 
7.8%
t951
 
7.8%
P950
 
7.8%
o944
 
7.7%
G59
 
0.5%
Other values (11)154
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)12218
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e3049
25.0%
l2000
16.4%
i1059
 
8.7%
D1049
 
8.6%
s1049
 
8.6%
r954
 
7.8%
t951
 
7.8%
P950
 
7.8%
o944
 
7.7%
G59
 
0.5%
Other values (11)154
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)12218
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e3049
25.0%
l2000
16.4%
i1059
 
8.7%
D1049
 
8.6%
s1049
 
8.6%
r954
 
7.8%
t951
 
7.8%
P950
 
7.8%
o944
 
7.7%
G59
 
0.5%
Other values (11)154
 
1.3%

Transmission
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.2 KiB
Manual
1133 
Automatic
926 

Length

Max length9
Median length6
Mean length7.3491986
Min length6

Characters and Unicode

Total characters15132
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowManual
2nd rowManual
3rd rowManual
4th rowManual
5th rowManual

Common Values

ValueCountFrequency (%)
Manual1133
55.0%
Automatic926
45.0%

Length

2025-12-07T11:19:37.425069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-07T11:19:37.773751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
manual1133
55.0%
automatic926
45.0%

Most occurring characters

ValueCountFrequency (%)
a3192
21.1%
u2059
13.6%
t1852
12.2%
M1133
 
7.5%
n1133
 
7.5%
l1133
 
7.5%
A926
 
6.1%
o926
 
6.1%
m926
 
6.1%
i926
 
6.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)15132
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a3192
21.1%
u2059
13.6%
t1852
12.2%
M1133
 
7.5%
n1133
 
7.5%
l1133
 
7.5%
A926
 
6.1%
o926
 
6.1%
m926
 
6.1%
i926
 
6.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)15132
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a3192
21.1%
u2059
13.6%
t1852
12.2%
M1133
 
7.5%
n1133
 
7.5%
l1133
 
7.5%
A926
 
6.1%
o926
 
6.1%
m926
 
6.1%
i926
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)15132
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a3192
21.1%
u2059
13.6%
t1852
12.2%
M1133
 
7.5%
n1133
 
7.5%
l1133
 
7.5%
A926
 
6.1%
o926
 
6.1%
m926
 
6.1%
i926
 
6.1%
Distinct77
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size16.2 KiB
2025-12-07T11:19:39.058603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length16
Median length11
Mean length6.7304517
Min length3

Characters and Unicode

Total characters13858
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.2%

Sample

1st rowPune
2nd rowLudhiana
3rd rowLucknow
4th rowMangalore
5th rowMumbai
ValueCountFrequency (%)
mumbai361
17.2%
delhi307
14.7%
pune144
 
6.9%
bangalore132
 
6.3%
hyderabad116
 
5.5%
lucknow78
 
3.7%
ahmedabad70
 
3.3%
chennai63
 
3.0%
kolkata60
 
2.9%
kanpur52
 
2.5%
Other values (70)711
34.0%
2025-12-07T11:19:41.117037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a2299
16.6%
i1100
 
7.9%
e982
 
7.1%
u898
 
6.5%
n892
 
6.4%
h751
 
5.4%
r725
 
5.2%
d636
 
4.6%
l634
 
4.6%
b631
 
4.6%
Other values (39)4310
31.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)13858
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a2299
16.6%
i1100
 
7.9%
e982
 
7.1%
u898
 
6.5%
n892
 
6.4%
h751
 
5.4%
r725
 
5.2%
d636
 
4.6%
l634
 
4.6%
b631
 
4.6%
Other values (39)4310
31.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)13858
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a2299
16.6%
i1100
 
7.9%
e982
 
7.1%
u898
 
6.5%
n892
 
6.4%
h751
 
5.4%
r725
 
5.2%
d636
 
4.6%
l634
 
4.6%
b631
 
4.6%
Other values (39)4310
31.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)13858
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a2299
16.6%
i1100
 
7.9%
e982
 
7.1%
u898
 
6.5%
n892
 
6.4%
h751
 
5.4%
r725
 
5.2%
d636
 
4.6%
l634
 
4.6%
b631
 
4.6%
Other values (39)4310
31.1%

Color
Categorical

Distinct17
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size16.2 KiB
White
802 
Silver
285 
Grey
220 
Blue
190 
Black
163 
Other values (12)
399 

Length

Max length6
Median length5
Mean length4.8266149
Min length3

Characters and Unicode

Total characters9938
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowGrey
2nd rowWhite
3rd rowMaroon
4th rowRed
5th rowGrey

Common Values

ValueCountFrequency (%)
White802
39.0%
Silver285
 
13.8%
Grey220
 
10.7%
Blue190
 
9.2%
Black163
 
7.9%
Red154
 
7.5%
Brown82
 
4.0%
Maroon37
 
1.8%
Gold30
 
1.5%
Bronze28
 
1.4%
Other values (7)68
 
3.3%

Length

2025-12-07T11:19:41.781122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
white802
39.0%
silver285
 
13.8%
grey220
 
10.7%
blue190
 
9.2%
black163
 
7.9%
red154
 
7.5%
brown82
 
4.0%
maroon37
 
1.8%
gold30
 
1.5%
bronze28
 
1.4%
Other values (7)68
 
3.3%

Most occurring characters

ValueCountFrequency (%)
e1771
17.8%
i1096
11.0%
t814
 
8.2%
h814
 
8.2%
W802
 
8.1%
r702
 
7.1%
l691
 
7.0%
B471
 
4.7%
S285
 
2.9%
v285
 
2.9%
Other values (19)2207
22.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)9938
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e1771
17.8%
i1096
11.0%
t814
 
8.2%
h814
 
8.2%
W802
 
8.1%
r702
 
7.1%
l691
 
7.0%
B471
 
4.7%
S285
 
2.9%
v285
 
2.9%
Other values (19)2207
22.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)9938
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e1771
17.8%
i1096
11.0%
t814
 
8.2%
h814
 
8.2%
W802
 
8.1%
r702
 
7.1%
l691
 
7.0%
B471
 
4.7%
S285
 
2.9%
v285
 
2.9%
Other values (19)2207
22.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)9938
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e1771
17.8%
i1096
11.0%
t814
 
8.2%
h814
 
8.2%
W802
 
8.1%
r702
 
7.1%
l691
 
7.0%
B471
 
4.7%
S285
 
2.9%
v285
 
2.9%
Other values (19)2207
22.2%

Owner
Categorical

Imbalance 

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size16.2 KiB
First
1619 
Second
373 
Third
 
42
UnRegistered Car
 
21
Fourth
 
3

Length

Max length16
Median length5
Mean length5.296746
Min length5

Characters and Unicode

Total characters10906
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowFirst
2nd rowSecond
3rd rowFirst
4th rowFirst
5th rowFirst

Common Values

ValueCountFrequency (%)
First1619
78.6%
Second373
 
18.1%
Third42
 
2.0%
UnRegistered Car21
 
1.0%
Fourth3
 
0.1%
4 or More1
 
< 0.1%

Length

2025-12-07T11:19:42.451161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-07T11:19:42.996549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
first1619
77.8%
second373
 
17.9%
third42
 
2.0%
unregistered21
 
1.0%
car21
 
1.0%
fourth3
 
0.1%
41
 
< 0.1%
or1
 
< 0.1%
more1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
r1708
15.7%
i1682
15.4%
t1643
15.1%
s1640
15.0%
F1622
14.9%
e437
 
4.0%
d436
 
4.0%
n394
 
3.6%
o378
 
3.5%
S373
 
3.4%
Other values (12)593
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)10906
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r1708
15.7%
i1682
15.4%
t1643
15.1%
s1640
15.0%
F1622
14.9%
e437
 
4.0%
d436
 
4.0%
n394
 
3.6%
o378
 
3.5%
S373
 
3.4%
Other values (12)593
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10906
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r1708
15.7%
i1682
15.4%
t1643
15.1%
s1640
15.0%
F1622
14.9%
e437
 
4.0%
d436
 
4.0%
n394
 
3.6%
o378
 
3.5%
S373
 
3.4%
Other values (12)593
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10906
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r1708
15.7%
i1682
15.4%
t1643
15.1%
s1640
15.0%
F1622
14.9%
e437
 
4.0%
d436
 
4.0%
n394
 
3.6%
o378
 
3.5%
S373
 
3.4%
Other values (12)593
 
5.4%

Seller Type
Categorical

Imbalance 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.2 KiB
Individual
1997 
Corporate
 
57
Commercial Registration
 
5

Length

Max length23
Median length10
Mean length10.003885
Min length9

Characters and Unicode

Total characters20598
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCorporate
2nd rowIndividual
3rd rowIndividual
4th rowIndividual
5th rowIndividual

Common Values

ValueCountFrequency (%)
Individual1997
97.0%
Corporate57
 
2.8%
Commercial Registration5
 
0.2%

Length

2025-12-07T11:19:43.874717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-07T11:19:44.440801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
individual1997
96.8%
corporate57
 
2.8%
commercial5
 
0.2%
registration5
 
0.2%

Most occurring characters

ValueCountFrequency (%)
i4009
19.5%
d3994
19.4%
a2064
10.0%
l2002
9.7%
n2002
9.7%
I1997
9.7%
v1997
9.7%
u1997
9.7%
o124
 
0.6%
r124
 
0.6%
Other values (10)288
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)20598
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i4009
19.5%
d3994
19.4%
a2064
10.0%
l2002
9.7%
n2002
9.7%
I1997
9.7%
v1997
9.7%
u1997
9.7%
o124
 
0.6%
r124
 
0.6%
Other values (10)288
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)20598
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i4009
19.5%
d3994
19.4%
a2064
10.0%
l2002
9.7%
n2002
9.7%
I1997
9.7%
v1997
9.7%
u1997
9.7%
o124
 
0.6%
r124
 
0.6%
Other values (10)288
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)20598
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i4009
19.5%
d3994
19.4%
a2064
10.0%
l2002
9.7%
n2002
9.7%
I1997
9.7%
v1997
9.7%
u1997
9.7%
o124
 
0.6%
r124
 
0.6%
Other values (10)288
 
1.4%

Engine
Real number (ℝ)

High correlation 

Distinct108
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1685.0155
Minimum624
Maximum6592
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.2 KiB
2025-12-07T11:19:45.203933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum624
5-th percentile998
Q11198
median1498
Q31995
95-th percentile2987
Maximum6592
Range5968
Interquartile range (IQR)797

Descriptive statistics

Standard deviation632.22009
Coefficient of variation (CV)0.37520134
Kurtosis7.1831544
Mean1685.0155
Median Absolute Deviation (MAD)301
Skewness1.8154107
Sum3469447
Variance399702.24
MonotonicityNot monotonic
2025-12-07T11:19:46.258693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1197231
 
11.2%
1498144
 
7.0%
1248122
 
5.9%
998121
 
5.9%
149784
 
4.1%
199582
 
4.0%
196882
 
4.0%
217973
 
3.5%
158256
 
2.7%
214351
 
2.5%
Other values (98)1013
49.2%
ValueCountFrequency (%)
6241
 
< 0.1%
7931
 
< 0.1%
79630
 
1.5%
79910
 
0.5%
81413
 
0.6%
9361
 
< 0.1%
9951
 
< 0.1%
998121
5.9%
99931
 
1.5%
10472
 
0.1%
ValueCountFrequency (%)
65923
0.1%
54611
 
< 0.1%
52041
 
< 0.1%
49512
0.1%
48061
 
< 0.1%
46632
0.1%
41632
0.1%
39821
 
< 0.1%
39021
 
< 0.1%
34983
0.1%

Drivetrain
Categorical

High correlation 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.2 KiB
FWD
1466 
RWD
321 
AWD
272 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6177
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFWD
2nd rowFWD
3rd rowFWD
4th rowFWD
5th rowRWD

Common Values

ValueCountFrequency (%)
FWD1466
71.2%
RWD321
 
15.6%
AWD272
 
13.2%

Length

2025-12-07T11:19:47.085195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-07T11:19:47.399043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
fwd1466
71.2%
rwd321
 
15.6%
awd272
 
13.2%

Most occurring characters

ValueCountFrequency (%)
W2059
33.3%
D2059
33.3%
F1466
23.7%
R321
 
5.2%
A272
 
4.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)6177
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
W2059
33.3%
D2059
33.3%
F1466
23.7%
R321
 
5.2%
A272
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6177
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
W2059
33.3%
D2059
33.3%
F1466
23.7%
R321
 
5.2%
A272
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6177
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
W2059
33.3%
D2059
33.3%
F1466
23.7%
R321
 
5.2%
A272
 
4.4%

Length
Real number (ℝ)

High correlation 

Distinct248
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4283.6314
Minimum3099
Maximum5569
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.2 KiB
2025-12-07T11:19:48.134893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3099
5-th percentile3565
Q13986
median4370
Q34620
95-th percentile4936
Maximum5569
Range2470
Interquartile range (IQR)634

Descriptive statistics

Standard deviation435.79913
Coefficient of variation (CV)0.10173591
Kurtosis-0.75525928
Mean4283.6314
Median Absolute Deviation (MAD)375
Skewness-0.0406491
Sum8819997
Variance189920.88
MonotonicityNot monotonic
2025-12-07T11:19:49.253019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3995221
 
10.7%
437096
 
4.7%
444070
 
3.4%
427057
 
2.8%
398555
 
2.7%
445644
 
2.1%
385042
 
2.0%
376540
 
1.9%
458539
 
1.9%
449036
 
1.7%
Other values (238)1359
66.0%
ValueCountFrequency (%)
30991
 
< 0.1%
339520
1.0%
34294
 
0.2%
34452
 
0.1%
349523
1.1%
35154
 
0.2%
35204
 
0.2%
353913
0.6%
354511
0.5%
356525
1.2%
ValueCountFrequency (%)
55692
 
0.1%
54621
 
< 0.1%
54531
 
< 0.1%
53991
 
< 0.1%
52651
 
< 0.1%
52551
 
< 0.1%
52523
0.1%
52471
 
< 0.1%
52465
0.2%
52263
0.1%

Width
Real number (ℝ)

High correlation 

Distinct170
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1768.0544
Minimum1475
Maximum2220
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.2 KiB
2025-12-07T11:19:50.219971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1475
5-th percentile1495
Q11695
median1770
Q31831
95-th percentile2031.1
Maximum2220
Range745
Interquartile range (IQR)136

Descriptive statistics

Standard deviation133.14641
Coefficient of variation (CV)0.075306739
Kurtosis1.0219556
Mean1768.0544
Median Absolute Deviation (MAD)75
Skewness0.3118235
Sum3640424
Variance17727.967
MonotonicityNot monotonic
2025-12-07T11:19:51.175443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1695201
 
9.8%
177093
 
4.5%
178062
 
3.0%
179061
 
3.0%
168051
 
2.5%
166051
 
2.5%
147549
 
2.4%
189049
 
2.4%
182046
 
2.2%
174546
 
2.2%
Other values (160)1350
65.6%
ValueCountFrequency (%)
147549
2.4%
149031
1.5%
149529
1.4%
15003
 
0.1%
15152
 
0.1%
15206
 
0.3%
15257
 
0.3%
155014
 
0.7%
15604
 
0.2%
157924
1.2%
ValueCountFrequency (%)
22204
 
0.2%
21831
 
< 0.1%
21731
 
< 0.1%
21578
0.4%
21551
 
< 0.1%
214119
0.9%
21391
 
< 0.1%
21209
0.4%
21101
 
< 0.1%
21051
 
< 0.1%

Height
Real number (ℝ)

High correlation 

Distinct196
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1590.2827
Minimum1165
Maximum1995
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.2 KiB
2025-12-07T11:19:52.214937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1165
5-th percentile1433
Q11485
median1545
Q31672
95-th percentile1844
Maximum1995
Range830
Interquartile range (IQR)187

Descriptive statistics

Standard deviation134.18687
Coefficient of variation (CV)0.08437926
Kurtosis0.14706665
Mean1590.2827
Median Absolute Deviation (MAD)76
Skewness0.87762518
Sum3274392
Variance18006.117
MonotonicityNot monotonic
2025-12-07T11:19:53.102321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
147588
 
4.3%
150580
 
3.9%
154580
 
3.9%
153063
 
3.1%
152061
 
3.0%
149558
 
2.8%
164056
 
2.7%
150051
 
2.5%
148546
 
2.2%
155544
 
2.1%
Other values (186)1432
69.5%
ValueCountFrequency (%)
11651
< 0.1%
12131
< 0.1%
12812
0.1%
12951
< 0.1%
12971
< 0.1%
13041
< 0.1%
13531
< 0.1%
13662
0.1%
13701
< 0.1%
13912
0.1%
ValueCountFrequency (%)
199520
1.0%
197510
0.5%
19402
 
0.1%
19309
0.4%
19251
 
< 0.1%
19225
 
0.2%
19202
 
0.1%
19011
 
< 0.1%
19001
 
< 0.1%
18953
 
0.1%

Seating Capacity
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.296746
Minimum2
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.2 KiB
2025-12-07T11:19:53.764769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q15
median5
Q35
95-th percentile7
Maximum8
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.81102947
Coefficient of variation (CV)0.15311844
Kurtosis2.7748654
Mean5.296746
Median Absolute Deviation (MAD)0
Skewness1.5153808
Sum10906
Variance0.6577688
MonotonicityNot monotonic
2025-12-07T11:19:54.525152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
51679
81.5%
7273
 
13.3%
442
 
2.0%
835
 
1.7%
623
 
1.1%
27
 
0.3%
ValueCountFrequency (%)
27
 
0.3%
442
 
2.0%
51679
81.5%
623
 
1.1%
7273
 
13.3%
835
 
1.7%
ValueCountFrequency (%)
835
 
1.7%
7273
 
13.3%
623
 
1.1%
51679
81.5%
442
 
2.0%
27
 
0.3%

Fuel Tank Capacity
Real number (ℝ)

High correlation 

Distinct55
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.892326
Minimum15
Maximum105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.2 KiB
2025-12-07T11:19:55.254481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile35
Q142
median50
Q360
95-th percentile80
Maximum105
Range90
Interquartile range (IQR)18

Descriptive statistics

Standard deviation14.696588
Coefficient of variation (CV)0.28321313
Kurtosis0.56977357
Mean51.892326
Median Absolute Deviation (MAD)10
Skewness0.89842882
Sum106846.3
Variance215.9897
MonotonicityNot monotonic
2025-12-07T11:19:56.181837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50245
 
11.9%
35222
 
10.8%
60155
 
7.5%
45154
 
7.5%
43146
 
7.1%
55117
 
5.7%
42111
 
5.4%
40106
 
5.1%
8092
 
4.5%
3789
 
4.3%
Other values (45)622
30.2%
ValueCountFrequency (%)
151
 
< 0.1%
275
 
0.2%
2830
 
1.5%
3227
 
1.3%
35222
10.8%
3789
4.3%
381
 
< 0.1%
40106
5.1%
416
 
0.3%
42111
5.4%
ValueCountFrequency (%)
1053
 
0.1%
1041
 
< 0.1%
10015
0.7%
952
 
0.1%
9318
0.9%
921
 
< 0.1%
9010
0.5%
855
 
0.2%
836
 
0.3%
82.53
 
0.1%

Max Power bhp
Real number (ℝ)

High correlation 

Distinct166
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.0829
Minimum35
Maximum660
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.2 KiB
2025-12-07T11:19:57.033925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile67
Q183
median116
Q3169
95-th percentile248
Maximum660
Range625
Interquartile range (IQR)86

Descriptive statistics

Standard deviation63.850696
Coefficient of variation (CV)0.49464874
Kurtosis9.0673137
Mean129.0829
Median Absolute Deviation (MAD)34
Skewness2.1317097
Sum265781.7
Variance4076.9113
MonotonicityNot monotonic
2025-12-07T11:19:57.962373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
89139
 
6.8%
6798
 
4.8%
11690
 
4.4%
8262
 
3.0%
7461
 
3.0%
8355
 
2.7%
12655
 
2.7%
17449
 
2.4%
17748
 
2.3%
18843
 
2.1%
Other values (156)1359
66.0%
ValueCountFrequency (%)
351
 
< 0.1%
392
 
0.1%
463
 
0.1%
4727
1.3%
481
 
< 0.1%
5310
 
0.5%
5511
0.5%
562
 
0.1%
5827
1.3%
595
 
0.2%
ValueCountFrequency (%)
6601
 
< 0.1%
6021
 
< 0.1%
5703
0.1%
5001
 
< 0.1%
4631
 
< 0.1%
4532
0.1%
4442
0.1%
3962
0.1%
3851
 
< 0.1%
3681
 
< 0.1%

Max Power rpm
Real number (ℝ)

High correlation 

Distinct40
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4809.1841
Minimum2910
Maximum8250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.2 KiB
2025-12-07T11:19:58.827659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2910
5-th percentile3500
Q14000
median4200
Q36000
95-th percentile6400
Maximum8250
Range5340
Interquartile range (IQR)2000

Descriptive statistics

Standard deviation1082.0616
Coefficient of variation (CV)0.224999
Kurtosis-1.4312561
Mean4809.1841
Median Absolute Deviation (MAD)800
Skewness0.27356675
Sum9902110
Variance1170857.4
MonotonicityNot monotonic
2025-12-07T11:19:59.605959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
4000458
22.2%
6000436
21.2%
4200153
 
7.4%
3750143
 
6.9%
5500113
 
5.5%
360097
 
4.7%
380079
 
3.8%
620076
 
3.7%
660068
 
3.3%
340062
 
3.0%
Other values (30)374
18.2%
ValueCountFrequency (%)
29108
 
0.4%
300016
 
0.8%
320011
 
0.5%
340062
3.0%
350047
 
2.3%
360097
4.7%
37003
 
0.1%
3750143
6.9%
380079
3.8%
39008
 
0.4%
ValueCountFrequency (%)
82503
 
0.1%
80001
 
< 0.1%
74001
 
< 0.1%
660068
3.3%
650014
 
0.7%
640035
1.7%
630032
1.6%
62504
 
0.2%
620076
3.7%
61001
 
< 0.1%

Max Torque Nm
Real number (ℝ)

High correlation 

Distinct141
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean244.06953
Minimum48
Maximum780
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.2 KiB
2025-12-07T11:20:00.269628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum48
5-th percentile90
Q1115
median200
Q3343
95-th percentile500
Maximum780
Range732
Interquartile range (IQR)228

Descriptive statistics

Standard deviation137.99343
Coefficient of variation (CV)0.56538573
Kurtosis0.37182314
Mean244.06953
Median Absolute Deviation (MAD)87
Skewness0.9298591
Sum502539.17
Variance19042.187
MonotonicityNot monotonic
2025-12-07T11:20:01.253624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200205
 
10.0%
400114
 
5.5%
11584
 
4.1%
11476
 
3.7%
9073
 
3.5%
25071
 
3.4%
38067
 
3.3%
19064
 
3.1%
35064
 
3.1%
32060
 
2.9%
Other values (131)1181
57.4%
ValueCountFrequency (%)
481
 
< 0.1%
542
 
0.1%
626
 
0.3%
6922
1.1%
7210
0.5%
7513
0.6%
7720
1.0%
788
 
0.4%
847
 
0.3%
852
 
0.1%
ValueCountFrequency (%)
7803
 
0.1%
7601
 
< 0.1%
7006
 
0.3%
6891
 
< 0.1%
62017
0.8%
6198
 
0.4%
60022
1.1%
5801
 
< 0.1%
56023
1.1%
5508
 
0.4%

Max Torque rpm
Real number (ℝ)

High correlation 

Distinct55
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2591.5881
Minimum150
Maximum6500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.2 KiB
2025-12-07T11:20:02.180066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum150
5-th percentile1400
Q11600
median1900
Q34000
95-th percentile4600
Maximum6500
Range6350
Interquartile range (IQR)2400

Descriptive statistics

Standard deviation1190.7864
Coefficient of variation (CV)0.45948132
Kurtosis-1.292422
Mean2591.5881
Median Absolute Deviation (MAD)400
Skewness0.59881066
Sum5336080
Variance1417972.1
MonotonicityNot monotonic
2025-12-07T11:20:03.363714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1750387
18.8%
4000251
12.2%
1500205
10.0%
1600166
 
8.1%
3500146
 
7.1%
1900135
 
6.6%
2000109
 
5.3%
140087
 
4.2%
460056
 
2.7%
420055
 
2.7%
Other values (45)462
22.4%
ValueCountFrequency (%)
1501
 
< 0.1%
120030
 
1.5%
125024
 
1.2%
13003
 
0.1%
13402
 
0.1%
13504
 
0.2%
13601
 
< 0.1%
13702
 
0.1%
140087
4.2%
145011
 
0.5%
ValueCountFrequency (%)
65001
 
< 0.1%
56001
 
< 0.1%
50003
 
0.1%
485025
1.2%
480039
1.9%
47501
 
< 0.1%
47003
 
0.1%
460056
2.7%
450053
2.6%
440036
1.7%

Age of Car
Real number (ℝ)

High correlation 

Distinct22
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.5745508
Minimum3
Maximum37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.2 KiB
2025-12-07T11:20:04.107450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4
Q16
median8
Q311
95-th percentile14
Maximum37
Range34
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.3635636
Coefficient of variation (CV)0.39227286
Kurtosis2.9266435
Mean8.5745508
Median Absolute Deviation (MAD)2
Skewness0.84068453
Sum17655
Variance11.31356
MonotonicityNot monotonic
2025-12-07T11:20:04.739662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
7268
13.0%
8262
12.7%
6218
10.6%
11192
9.3%
9187
9.1%
10178
8.6%
4156
7.6%
5132
6.4%
12128
6.2%
1392
 
4.5%
Other values (12)246
11.9%
ValueCountFrequency (%)
381
 
3.9%
4156
7.6%
5132
6.4%
6218
10.6%
7268
13.0%
8262
12.7%
9187
9.1%
10178
8.6%
11192
9.3%
12128
6.2%
ValueCountFrequency (%)
371
 
< 0.1%
291
 
< 0.1%
251
 
< 0.1%
231
 
< 0.1%
211
 
< 0.1%
192
 
0.1%
186
 
0.3%
1713
 
0.6%
1633
1.6%
1527
1.3%

Volume
Real number (ℝ)

High correlation 

Distinct347
Distinct (%)16.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2153998 × 1010
Minimum7.4613612 × 109
Maximum2.1236875 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.2 KiB
2025-12-07T11:20:05.485492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum7.4613612 × 109
5-th percentile8.5859104 × 109
Q11.0344445 × 1010
median1.1727722 × 1010
Q31.3481387 × 1010
95-th percentile1.6684498 × 1010
Maximum2.1236875 × 1010
Range1.3775514 × 1010
Interquartile range (IQR)3.1369423 × 109

Descriptive statistics

Standard deviation2.5343121 × 109
Coefficient of variation (CV)0.20851675
Kurtosis-0.10164928
Mean1.2153998 × 1010
Median Absolute Deviation (MAD)1.639406 × 109
Skewness0.59052571
Sum2.5025081 × 1013
Variance6.4227378 × 1018
MonotonicityNot monotonic
2025-12-07T11:20:06.193999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.1727722 × 1010103
 
5.0%
1.1251071 × 101054
 
2.6%
949984800040
 
1.9%
998439750039
 
1.9%
1.2388978 × 101039
 
1.9%
1.039953495 × 101035
 
1.7%
1.052972138 × 101035
 
1.7%
1.555376475 × 101034
 
1.7%
1.15350345 × 101033
 
1.6%
1.04569125 × 101030
 
1.5%
Other values (337)1617
78.5%
ValueCountFrequency (%)
746136125020
1.0%
75264825008
 
0.4%
76537242601
 
< 0.1%
76982831252
 
0.1%
779102375011
0.5%
779567000010
0.5%
79315879501
 
< 0.1%
79614750003
 
0.1%
812587500013
0.6%
82268575001
 
< 0.1%
ValueCountFrequency (%)
2.12368752 × 10101
 
< 0.1%
2.047502268 × 10103
 
0.1%
2.044142256 × 10101
 
< 0.1%
2.03644263 × 10102
 
0.1%
1.883875792 × 10101
 
< 0.1%
1.88205325 × 10105
0.2%
1.867657573 × 10101
 
< 0.1%
1.859511 × 10101
 
< 0.1%
1.853019228 × 101012
0.6%
1.847251374 × 10107
0.3%

Interactions

2025-12-07T11:19:08.601581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:16:35.934595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:16:47.562059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:16:58.424013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:10.459795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:21.181779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:32.476641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:43.365408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:53.761060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:04.388684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:15.818830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:26.084692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:36.492292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:47.718338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:58.574148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:19:09.467476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:16:36.685586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:16:48.397108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:16:59.237813image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:11.707760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:22.807471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:33.243471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:44.061349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:54.573466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:05.156519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:16.541308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:26.859407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:37.426327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:48.524655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:59.386097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:19:10.197642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:16:37.579168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:16:49.181422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:00.135707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:12.427224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:23.482179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:33.966779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:44.726599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:55.287269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:05.844845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:17.225393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:27.572598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:38.172232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:49.231524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:19:00.085644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:19:10.966604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:16:38.483187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:16:49.947561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:00.830312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:13.133908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:24.181884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:34.739790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:45.427520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:56.022417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:06.544846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:17.953466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:28.303984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:38.915065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:49.964102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:19:00.807190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:19:11.653520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:16:39.426919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:16:50.666673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:01.521458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:13.719850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:24.859212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:35.458408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:46.057638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:56.688910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:07.188468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:18.630713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:29.052283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:39.636157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:50.664956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:19:01.494401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:19:12.330905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:16:40.184981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:16:51.340881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:02.140118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:14.400060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:25.495599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:36.084026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:46.692138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:57.342340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:07.786532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:19.325628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:29.625064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:40.360370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:51.379447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:19:02.165266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:19:13.118971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:16:40.930836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:16:52.093784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:02.915422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:15.097895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:26.167924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:36.672802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:47.678131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:58.031401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:08.489152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:20.078542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:30.214773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:41.085450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:52.136411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:19:02.949569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:19:13.807638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:16:41.638961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:16:52.778961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:04.008702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:15.755369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:26.778248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:37.366088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:48.294167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:58.675743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:09.147789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:20.733798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:30.881240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:41.747388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:52.867787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:19:03.596506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:19:14.502794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:16:42.407208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:16:53.472179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:04.857149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:16.448380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:27.438051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:38.104439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:48.920937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:59.352609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:09.849249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:21.454982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:31.555266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:42.475161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:53.570357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:19:04.272249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:19:15.194223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:16:43.140454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:16:54.193751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:05.794134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:17.122466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:28.098337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:38.809707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:49.566979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:00.007414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:10.457372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:22.193496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:32.247688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:43.231584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:54.234519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:19:04.958896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:19:15.887767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:16:43.812712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:16:54.885407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:06.558657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:17.789228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:28.779413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:39.473828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:50.206143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:00.721833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:11.113281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:22.852558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:32.948301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:43.946552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:54.957396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:19:05.659465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:19:16.601878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:16:44.541399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:16:55.585746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:07.257295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:18.446011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:29.461919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:40.145463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:50.843172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:01.484222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:11.815618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:23.352258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:33.602046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:44.681528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:55.661050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:19:06.357283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:19:17.391537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:16:45.308192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:16:56.329023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:07.998580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:18.994540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:30.190635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:40.943299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:51.633367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:02.189715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:12.495899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:23.943830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:34.330175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:45.410387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:56.401262image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:19:07.099788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:19:19.352632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:16:46.069959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:16:57.029607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:08.780081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:19.623993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:30.906257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:41.668718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:52.285081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:02.920988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:13.201015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:24.628654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:35.020874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:46.124032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:57.100690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:19:07.663249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:19:20.257368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:16:46.795624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:16:57.677174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:09.549187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:20.289915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:31.582145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:42.397951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:17:52.903479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:03.617007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:14.940889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:25.287637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:35.710945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:46.862607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:18:57.662791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:19:08.110411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-12-07T11:20:07.011140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Age of CarColorDrivetrainEngineFuel Tank CapacityFuel TypeHeightKilometerLengthMakeMax Power bhpMax Power rpmMax Torque NmMax Torque rpmOwnerPriceSeating CapacitySeller TypeTransmissionVolumeWidthYear
Age of Car1.0000.1920.054-0.038-0.0960.359-0.1380.602-0.0970.088-0.1650.055-0.1240.1600.193-0.5200.0030.0000.165-0.173-0.210-1.000
Color0.1921.0000.1880.1090.1010.0720.1160.0580.0980.1390.1060.0910.1050.0880.0000.1060.1020.0000.2580.1040.1160.192
Drivetrain0.0540.1881.0000.5660.5600.2810.4840.1120.4680.6050.5360.3510.5950.3460.0660.4650.3670.0020.4290.5780.5250.054
Engine-0.0380.1090.5661.0000.8330.2390.2190.1180.8580.5260.882-0.5730.891-0.5340.1220.7190.3440.0580.5240.8660.8340.038
Fuel Tank Capacity-0.0960.1010.5600.8331.0000.1810.2640.0770.8020.4280.824-0.5380.851-0.5580.0460.7250.3020.0000.5150.8640.8590.096
Fuel Type0.3590.0720.2810.2390.1811.0000.1670.1230.1980.2560.1550.3330.2680.3000.1160.1360.1390.0000.2060.2070.2370.359
Height-0.1380.1160.4840.2190.2640.1671.0000.0820.0630.5340.071-0.2950.177-0.2280.0560.1280.5610.0700.3060.4660.2860.138
Kilometer0.6020.0580.1120.1180.0770.1230.0821.0000.0570.112-0.040-0.2140.079-0.0600.147-0.2790.1950.0000.1810.070-0.005-0.602
Length-0.0970.0980.4680.8580.8020.1980.0630.0571.0000.4430.887-0.4540.840-0.4750.0780.7460.3180.0810.5630.8680.8390.097
Make0.0880.1390.6050.5260.4280.2560.5340.1120.4431.0000.5900.4550.5230.5070.0870.3530.4350.1370.6780.3730.4110.088
Max Power bhp-0.1650.1060.5360.8820.8240.1550.071-0.0400.8870.5901.000-0.4110.903-0.5160.0920.8310.1530.0850.6500.8320.8720.165
Max Power rpm0.0550.0910.351-0.573-0.5380.333-0.295-0.214-0.4540.455-0.4111.000-0.6970.7250.070-0.419-0.3740.1030.261-0.567-0.552-0.055
Max Torque Nm-0.1240.1050.5950.8910.8510.2680.1770.0790.8400.5230.903-0.6971.000-0.7020.0760.7920.2440.0570.5710.8530.8920.124
Max Torque rpm0.1600.0880.346-0.534-0.5580.300-0.228-0.060-0.4750.507-0.5160.725-0.7021.0000.055-0.541-0.2590.0850.299-0.569-0.588-0.160
Owner0.1930.0000.0660.1220.0460.1160.0560.1470.0780.0870.0920.0700.0760.0551.0000.0850.0430.0000.0780.0620.0430.193
Price-0.5200.1060.4650.7190.7250.1360.128-0.2790.7460.3530.831-0.4190.792-0.5410.0851.0000.1380.0990.6780.7470.8120.520
Seating Capacity0.0030.1020.3670.3440.3020.1390.5610.1950.3180.4350.153-0.3740.244-0.2590.0430.1381.0000.0000.1430.4770.277-0.003
Seller Type0.0000.0000.0020.0580.0000.0000.0700.0000.0810.1370.0850.1030.0570.0850.0000.0990.0001.0000.1080.0340.0340.000
Transmission0.1650.2580.4290.5240.5150.2060.3060.1810.5630.6780.6500.2610.5710.2990.0780.6780.1430.1081.0000.4380.5630.165
Volume-0.1730.1040.5780.8660.8640.2070.4660.0700.8680.3730.832-0.5670.853-0.5690.0620.7470.4770.0340.4381.0000.9270.173
Width-0.2100.1160.5250.8340.8590.2370.286-0.0050.8390.4110.872-0.5520.892-0.5880.0430.8120.2770.0340.5630.9271.0000.210
Year-1.0000.1920.0540.0380.0960.3590.138-0.6020.0970.0880.165-0.0550.124-0.1600.1930.520-0.0030.0000.1650.1730.2101.000

Missing values

2025-12-07T11:19:21.642170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-12-07T11:19:22.975573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

MakeModelPriceYearKilometerFuel TypeTransmissionLocationColorOwnerSeller TypeEngineDrivetrainLengthWidthHeightSeating CapacityFuel Tank CapacityMax Power bhpMax Power rpmMax Torque NmMax Torque rpmAge of CarVolume
0HondaAmaze 1.2 VX i-VTEC505000.000201787150.000PetrolManualPuneGreyFirstCorporate1198.000FWD3990.0001680.0001505.0005.00035.00087.0006000.000109.0004500.000810088316000.000
1Maruti SuzukiSwift DZire VDI450000.000201475000.000DieselManualLudhianaWhiteSecondIndividual1248.000FWD3995.0001695.0001555.0005.00042.00074.0004000.000190.0002000.0001110529721375.000
2Hyundaii10 Magna 1.2 Kappa2220000.000201167000.000PetrolManualLucknowMaroonFirstIndividual1197.000FWD3585.0001595.0001550.0005.00035.00079.0006000.000112.7624000.000148863016250.000
3ToyotaGlanza G799000.000201937500.000PetrolManualMangaloreRedFirstIndividual1197.000FWD3995.0001745.0001510.0005.00037.00082.0006000.000113.0004200.000610526625250.000
4ToyotaInnova 2.4 VX 7 STR [2016-2020]1950000.000201869000.000DieselManualMumbaiGreyFirstIndividual2393.000RWD4735.0001830.0001795.0007.00055.000148.0003400.000343.0001400.000715553764750.000
5Maruti SuzukiCiaz ZXi675000.000201773315.000PetrolManualPuneGreyFirstIndividual1373.000FWD4490.0001730.0001485.0005.00043.00091.0006000.000130.0004000.000811535034500.000
6Mercedes-BenzCLA 200 Petrol Sport1898999.000201547000.000PetrolAutomaticMumbaiWhiteSecondIndividual1991.000FWD4630.0001777.0001432.0005.00050.000181.0005500.000300.0001200.0001011781794320.000
7BMWX1 xDrive20d M Sport2650000.000201775000.000DieselAutomaticCoimbatoreWhiteSecondIndividual1995.000AWD4439.0001821.0001612.0005.00051.000188.0004000.000400.0001750.000813030471428.000
8SkodaOctavia 1.8 TSI Style Plus AT [2017]1390000.000201756000.000PetrolAutomaticMumbaiWhiteFirstIndividual1798.000FWD4670.0001814.0001476.0005.00050.000177.0005100.000250.0001250.000812503756880.000
9NissanTerrano XL (D)575000.000201585000.000DieselManualMumbaiWhiteFirstIndividual1461.000FWD4331.0001822.0001671.0005.00050.00084.0003750.000200.0001900.0001013185998022.000
MakeModelPriceYearKilometerFuel TypeTransmissionLocationColorOwnerSeller TypeEngineDrivetrainLengthWidthHeightSeating CapacityFuel Tank CapacityMax Power bhpMax Power rpmMax Torque NmMax Torque rpmAge of CarVolume
2049Mercedes-BenzGLS 400 4MATIC5950000.000201780000.000PetrolAutomaticDelhiBlackFirstIndividual2996.000AWD5130.0001934.0001850.0005.000100.000329.0005250.000480.0001600.000818354627000.000
2050HyundaiCreta SX Plus 1.6 Petrol891000.000201647000.000PetrolManualDelhiWhiteFirstIndividual1591.000FWD4270.0001780.0001630.0005.00060.000122.0006400.000154.0004850.000912388978000.000
2051Maruti SuzukiVitara Brezza VXi925000.000202148000.000PetrolManualBangaloreWhiteFirstIndividual1462.000FWD3995.0001790.0001640.0005.00048.000103.0006000.000138.0004400.000411727722000.000
2052Hyundaii20 Sportz 1.4 CRDI409999.000201468000.000DieselManualAgraSilverFirstIndividual1396.000FWD3940.0001710.0001505.0005.00045.00090.0004000.000220.0001750.0001110139787000.000
2053Maruti SuzukiRitz Vxi (ABS) BS-IV245000.000201479000.000PetrolManualFaridabadWhiteSecondIndividual1197.000FWD3775.0001680.0001620.0005.00043.00085.0006000.000113.0004500.0001110274040000.000
2054MahindraXUV500 W8 [2015-2017]850000.000201690300.000DieselManualSuratWhiteFirstIndividual2179.000FWD4585.0001890.0001785.0007.00070.000138.0003750.000330.0001600.000915468185250.000
2055HyundaiEon D-Lite +275000.000201483000.000PetrolManualAhmedabadWhiteSecondIndividual814.000FWD3495.0001550.0001500.0005.00032.00055.0005500.00075.0004000.000118125875000.000
2056FordFigo Duratec Petrol ZXI 1.2240000.000201373000.000PetrolManualThaneSilverFirstIndividual1196.000FWD3795.0001680.0001427.0005.00045.00070.0006250.000102.0004000.000129097981200.000
2057BMW5-Series 520d Luxury Line [2017-2019]4290000.000201860474.000DieselAutomaticCoimbatoreWhiteFirstIndividual1995.000RWD4936.0001868.0001479.0005.00065.000188.0004000.000400.0001750.000713637042592.000
2058MahindraBolero Power Plus ZLX [2016-2019]670000.000201772000.000DieselManualGuwahatiWhiteFirstIndividual1493.000RWD3995.0001745.0001880.0007.00050.00070.0003600.000195.0001400.000813105997000.000